A Bayesian model is a model that is generated based on priors and gathered data. Specifically, the Bayesian model may include one or more parameters that are modeled with posterior distributions. The posteriors distributions may be based on prior distributions for the one or more parameters and data gathered for the one or more parameters. A prior is a probability distribution that models one of the parameters, it is a belief regarding a parameter before data is gathered for said parameter. Priors that are based on knowledge of what the parameter should be are referred to as informative or strong priors while priors that are not based on knowledge are referred to as non-informative or weak priors. Experiments (or otherwise data collection) can be performed for the various parameters. Based on the collected or experimental data and the priors, posteriors can be determined. A posterior may be a probability distribution that is based on both the prior and the collected data.